A monitor apparatus, method, and non-transitory computer readable storage medium thereof are provided. The monitor method is adapted for an electronic computing apparatus, wherein the electronic computing apparatus stores a smart contract and a blockchain ledger of a blockchain system. The monitor method periodically executes the following steps: (a) obtaining a piece of behavior information of a first electronic apparatus at a time point, (b) retrieving, via the smart contract, a plurality of pieces of previous behavior information within a time interval from the blockchain ledger, wherein the time interval is defined by the time point, and each piece of previous behavior information corresponds to one of a plurality of second electronic apparatuses and the first electronic apparatus, (c) determining a legality of the piece of behavior information according to the pieces of previous behavior information, and (d) writing the behavior information into the blockchain ledger.

Disclosed are a system of defending against a DDoS attack based on an SDN and a method thereof. According to the present invention, when the HTTP Request message suspected for the attack arrives at the web server, the web server sends the HTTP Request message to the SDN controller located in the network, and the SDN controller determines the DDoS attack instead of the web server which is the attack target and blocks the traffic from the attacker through the nodes on the network according to the determination result of the SDN controller. Thereby, the traffic suspected as the DDoS attack that exhausts available connection resources of the web server is input to the SDN controller instead of the web server. Thereby the web server can be protected from the DDoS attack and the maintenance of the normal operation of the web server can be secured.

Embodiments are directed to monitoring network traffic over a network using one or more network monitoring computers. A monitoring engine may be instantiated to perform actions, including: monitoring network traffic to identify client requests provided by clients and server responses provided by servers in response to the client requests; determining request metrics associated with the client requests; and determining response metrics associated with the server responses. An analysis engine may be instantiated that performs actions, including: comparing the request metrics with the response metrics; determining atypical behavior associated with the clients based on the comparison such that the atypical behavior includes an absence of adaption by the clients to changes in the server responses; and providing alerts that may identify the clients be associated with the atypical behavior.

Methods and systems for detecting and responding to Denial of Service (DoS) attacks comprise: detecting a DoS attack or potential DoS attack against a first server system comprising one or more servers; receiving, at a second server system comprising one or more servers, network traffic directed to the first server system; subjecting requesting clients to one or more challenge mechanisms, the challenge mechanisms including one or more of challenging requesting clients to follow through HTTP redirect responses, challenging requesting clients to request Secure Sockets Layer (SSL) session resumption, or challenging requesting clients to store and transmit HTTP cookies; identifying one or more non-suspect clients; and forwarding, by the second server system, traffic corresponding to the one or more non-suspect clients to the first server system. Once a client has been validated, clients may communicate directly with application servers in a secure manner by transparently passing through intermediary proxy servers.

Methods and systems for mitigating a spoofing-based attack include calculating a travel distance between a source Internet Protocol (IP) address and a target IP address from a received packet based on time-to-live information from the received packet. An expected travel distance between the source IP address and the target IP address is estimated based on a sparse set of known source/target distances. It is determined that the received packet has a spoofed source IP address based on a comparison between the calculated travel distance and the expected travel distance. A security action is performed responsive to the determination that the received packet has a spoofed source IP address.

Endpoint security systems and methods include a distance estimation module configured to calculate a travel distance between a source Internet Protocol (IP) address and an IP address for a target network endpoint system from a received packet received by the target network endpoint system based on time-to-live (TTL) information from the received packet. A machine learning model is configured to estimate an expected travel distance between the source IP address and the target network endpoint system IP address based on a sparse set of known source/target distances. A spoof detection module is configured to determine that the received packet has a spoofed source IP address based on a comparison between the calculated travel distance and the expected travel distance. A security module is configured to perform a security action at the target network endpoint system responsive to the determination that the received packet has a spoofed source IP address.

Endpoint security systems and methods include a distance estimation module configured to calculate a travel distance between a source Internet Protocol (IP) address and an IP address for a target network endpoint system from a received packet received by a network gateway system based on time-to-live (TTL) information from the received packet. A machine learning model is configured to estimate an expected travel distance between the source IP address and the target network endpoint system IP address based on a sparse set of known source/target distances. A spoof detection module is configured to determine that the received packet has a spoofed source IP address based on a comparison between the calculated travel distance and the expected travel distance. A security module is configured to perform a security action at the network gateway system responsive to the determination that the received packet has a spoofed source IP address.

A system for detecting a targeted attack by a first machine on a second machine is provided. The system includes an application including instructions to: according to first parameters, group alerts for attacking machines; each group of alerts corresponds to attacks performed by a respective one of the attacking machines, and each of the alerts is indicative of a possible attack performed by one of the attacking machines; according to second parameters, group metadata corresponding to attacked machines implementing cloud applications; based on the group of metadata corresponding to the second machine and one or more co-factors, evaluate one or more alerts corresponding to attacks performed by the first machine on the second machine relative to alerts associated with attacks performed by the first machine on other machines or attacks performed by the attacking machines; and alert the second machine of the targeted attack.

A server receives internet traffic from a client device. The server is one of multiple servers of a distributed cloud computing network which are each associated with a set of server identity(ies) including a server/data center certification identity. The server processes, at layer 3, the internet traffic including participating in a layer 3 DDoS protection service. If the traffic is not dropped by the layer 3 DDoS protection service, further processing is performed. The server determines whether it is permitted to process the traffic at layers 5-7 including whether it is associated with a server/data center certification identity that meets a selected criteria for the destination of the internet traffic. If the server does not meet the criteria, it transmits the traffic to another one of the multiple servers for processing the traffic at layers 5-7.

Obtain, by a controller, from at least one provisioning database of an internet service provider, assigned bandwidth per customer for a plurality of internet service provider customers. Obtain, by the controller, from a plurality of peering entry points of the internet service provider, currently used bandwidth per customer for the plurality of internet service provider customers. Compare, by the controller, for the plurality of internet service provider customers, the assigned bandwidth per customer to the currently used bandwidth per customer, to determine at least one given customer of the plurality of internet service provider customers putatively suffering from a distributed denial of service attack. Initiate at least one remedial action for the at least one given customer of the plurality of internet service provider customers putatively suffering from the distributed denial of service attack.